Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
Monitoring Cloud Foundry: Learning about the FirehoseDustin Ruehle
Distributed and massively scalable systems are difficult to design, implement, and operate. Further, microservice architectures are supposed to enable your business to be disruptive and innovative. Cloud Foundry provides that solution for modern, cloud native architectures and accelerates the ability to transform operational and development practices. The aggregated logging subsystem of Cloud Foundry, the Loggregator, is a key component of this transformation. And within the Loggregator, the firehose is where log and metric data is consumed. This talk will walk through how to consume the firehose and dive into the anatomy of the various firehose message types. Several use cases and lessons learned monitoring the platform versus an application will be covered to further demonstrate how Cloud Foundry can facilitate innovation.
Thoughts on heptio's ark - Contributors Meet 21st Sept 2018OpenEBS
Create a OpenEBS ARK Plugin that will implement the Block-Store API exposed by ARK
Backup Operation
ARK will invoke the Plugin-Snapshot (Backup) method.
Plugin: will call maya-apiserver backup api on a given volume
Maya-apiserver backup will call volumes (jiva/cstor) backup api
(jiva/cstor) volume controller will take a snapshot, and pass the request to one of the replica’s to push snapshot data to remote backup location (say S3 compatible -- as passed via the ark plugin or a custom backup location on mayaonline or may be a nfs server that openebs supports ). The code to actually push the data to backup location can make use of restic. We are putting it at the jiva/cstor for getting access to snapshot/incr snapshot data.
Restore Operation
ARK will invoke the Plugin-VolumeFromSnapshot (Restore) method
Plugin will invoke maya-apiserver to create a new PV/PVC and restore the data from backup.
ARK will launch the application with the PV/PVC.
This is a fairly easy project, but worth while. I created this scraper using free API to get weather information for a data warehouse. With detailed weather information by date and zip codes at your disposal, you can tie this with location information in your database or data warehouse to do extensive querying/analytics. E.g.
• How does rain affect my sales by region
• How does humidity affect sales
• How does cloud cover affect sales
• How does weather affect tips
• How does weather affect Employee productivity
The sky (pun intended) is virtually the limit on this.
Good Luck.
CCAFS Science Meeting presentation by Gerald Nelson (Senior Research Fellow , IFPRI) - "From Global Futures to Strategic Foresight: Moving Beyond Norman Borlaug"
A review of the relationship between land tenure and tropical deforestation. Presentation by Brian E.obinson,
Margaret B. Holland and Lisa Naughton-Treves.
Monitoring Cloud Foundry: Learning about the FirehoseDustin Ruehle
Distributed and massively scalable systems are difficult to design, implement, and operate. Further, microservice architectures are supposed to enable your business to be disruptive and innovative. Cloud Foundry provides that solution for modern, cloud native architectures and accelerates the ability to transform operational and development practices. The aggregated logging subsystem of Cloud Foundry, the Loggregator, is a key component of this transformation. And within the Loggregator, the firehose is where log and metric data is consumed. This talk will walk through how to consume the firehose and dive into the anatomy of the various firehose message types. Several use cases and lessons learned monitoring the platform versus an application will be covered to further demonstrate how Cloud Foundry can facilitate innovation.
Thoughts on heptio's ark - Contributors Meet 21st Sept 2018OpenEBS
Create a OpenEBS ARK Plugin that will implement the Block-Store API exposed by ARK
Backup Operation
ARK will invoke the Plugin-Snapshot (Backup) method.
Plugin: will call maya-apiserver backup api on a given volume
Maya-apiserver backup will call volumes (jiva/cstor) backup api
(jiva/cstor) volume controller will take a snapshot, and pass the request to one of the replica’s to push snapshot data to remote backup location (say S3 compatible -- as passed via the ark plugin or a custom backup location on mayaonline or may be a nfs server that openebs supports ). The code to actually push the data to backup location can make use of restic. We are putting it at the jiva/cstor for getting access to snapshot/incr snapshot data.
Restore Operation
ARK will invoke the Plugin-VolumeFromSnapshot (Restore) method
Plugin will invoke maya-apiserver to create a new PV/PVC and restore the data from backup.
ARK will launch the application with the PV/PVC.
This is a fairly easy project, but worth while. I created this scraper using free API to get weather information for a data warehouse. With detailed weather information by date and zip codes at your disposal, you can tie this with location information in your database or data warehouse to do extensive querying/analytics. E.g.
• How does rain affect my sales by region
• How does humidity affect sales
• How does cloud cover affect sales
• How does weather affect tips
• How does weather affect Employee productivity
The sky (pun intended) is virtually the limit on this.
Good Luck.
CCAFS Science Meeting presentation by Gerald Nelson (Senior Research Fellow , IFPRI) - "From Global Futures to Strategic Foresight: Moving Beyond Norman Borlaug"
A review of the relationship between land tenure and tropical deforestation. Presentation by Brian E.obinson,
Margaret B. Holland and Lisa Naughton-Treves.
Presentation by Julian Ramirez-Villegas.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
Space Systems & Space Subsystems Fundamentals Technical Training Course SamplerJim Jenkins
This four-day course in space systems and space subsystems is for technical and management personnel who wish to gain an understanding of the important technical concepts in the development of space instrumentation, subsystems, and systems. The goal is to assist students to achieve their professional potential by endowing them with an understanding of the subsystems and supporting disciplines important to developing space instrumentation, space subsystems, and space systems. It designed for participants who expect to plan, design, build, integrate, test, launch, operate or manage subsystems, space systems, launch vehicles, spacecraft, payloads, or ground systems. The objective is to expose each participant to the fundamentals of each subsystem and their inter-relations, to not necessarily make each student a systems engineer, but to give aerospace engineers and managers a technically based space systems perspective. The fundamental concepts are introduced and illustrated by state-of-the-art examples. This course differs from the typical space systems course in that the technical aspects of each important subsystem are addressed.
InfluxDB IOx Tech Talks: Query Processing in InfluxDB IOxInfluxData
Query Processing in InfluxDB IOx
InfluxDB IOx Query Processing: In this talk we will provide an overview of Query Execution in IOx describing how once data is ingested that it is queryable, both via SQL and Flux and InfluxQL (via storage gRPC APIs).
Slides from my Introduction to PostGIS workshop at the FOSS4G conference in 2009. The material is available at http://revenant.ca/www/postgis/workshop/
This presentation deals with the parameterisation (modelling) of net long wave radiation. It is deemed useful for estimation of both snow cover evolution and evapotranspiration
DO NOT use System.exit().DO NOT add the project or package stateme.pdfinfo48697
DO NOT use System.exit().
DO NOT add the project or package statements.
DO NOT change the class name.
DO NOT change the headers of ANY of the given methods.
DO NOT add any new class fields.
ONLY display the result as specified by the example for each problem.
DO NOT print other messages, follow the examples for each problem.
USE StdIn, StdOut, and StdRandom libraries.
No SCANNER PLEASE!
/*
*
* @author
*
* To generate weather for location at longitude -98.76 and latitude 26.70 for
* the month of February do:
* java WeatherGenerator -98.76 26.70 3
*/
public class WeatherGenerator {
static final int WET = 1; // Use this value to represent a wet day
static final int DRY = 2; // Use this value to represent a dry day
// Number of days in each month, January is index 0, February is index 1...
static final int[] numberOfDaysInMonth = {31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31};
/*
* Description:
* this method works under the assumption that under the same directory as
WeatherGenerator.java,
* there exist drywet.txt and wetwet.txt that contains probabilities of the next day being wet
* with today being a dry/wet day.
* Parameters:
* drywet & wetwet:
* 2 empty 2D arrays that will be populated, with each row in the format of:
* {Longitude, Latitude, January, February, March, April, May, June, July, August, September,
October, November, December}
* {-97.58, 26.02, 0.76, 0.75, 0.77, 0.74, 0.80, 0.86, 0.94, 0.97, 0.89, 0.77, 0.74, 0.77}
* you can assume that there more than enough data in the txt file,
* when there are more data in the txt files than what drywet & wetwet can store, store it up to the
array size
* Return:
* this method does not return data, the method is used to populate two 2D arrays with 14
* columns - drywet and wetwet.
* Example:
* double[][] drywet = new double[4100][14];
* double[][] wetwet = new double[4100][14];
* populateArrays(drywet, wetwet);
*/
public static void populateArrays(double[][] drywet, double[][] wetwet) {
StdIn.setFile("drywet.txt");
for(int i=0; i < drywet.length; i++){
for(int j=0; j<14; j++){
drywet[i][j] = StdIn.readDouble();
}
}
StdIn.setFile("wetwet.txt");
for(int i=0; i < drywet.length; i++){
for(int j=0; j<14; j++){
wetwet[i][j] = StdIn.readDouble();
}
}
}
/*
* Description:
* this method uses drywet and wetwet arrays populated by populateArrays, and longitude and
latitude
* of the target location to populate drywetProbability and wetwetProbability with the
* probability of dry/wet day is followed by a wet day each month at that location.
* In other words, extracting the probabilities of the location.
* parameters:
* drywetProbability: array of size 12 that will be populated with by the probability of dry days
* followed by a wet day each month
* wetwetProbability: array of size 12 that will be populated with by the probability of wet days
* followed by a wet day each month
* longitude:
* the longitude of the location
* latitude:
* the latitude of the location
* drywet:
* a 2D array of doubles populated b.
The Accelerating Impact of CGIAR Climate Research for Africa (AICCRA) project works to deliver a climate-smart African future driven by science and innovation in agriculture.
AICCRA does this by enhancing access to climate information services and climate-smart agricultural technology to millions of smallholder farmers in Africa.
With better access to climate technology and advisory services—linked to information about effective response measures—farmers can better anticipate climate-related events and take preventative action that help communities better safeguard their livelihoods and the environment.
AICCRA is supported by a grant from the International Development Association (IDA) of the World Bank, which is used to enhance research and capacity-building activities by the CGIAR centers and initiatives as well as their partners in Africa.
About IDA: IDA helps the world’s poorest countries by providing grants and low to zero-interest loans for projects and programmes that boost economic growth, reduce poverty, and improve poor people’s lives.
IDA is one of the largest sources of assistance for the world’s 76 poorest countries, 39 of which are in Africa.
Annual IDA commitments have averaged about $21 billion over circa 2017-2020, with approximately 61 percent going to Africa.
This presentation was given on 27 October 2021 by Mengpin Ge, Global Climate Program Associate at WRI, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was given on 27 October 2021 by Sabrina Rose, Policy Consultant at CCAFS, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was given on 27 October 2021 by Krystal Crumpler, Climate Change and Agricultural Specialist at FAO, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was meant to be included in the 2021 CLIFF-GRADS Welcome Webinar and presented by Ciniro Costa Jr. (CCAFS).
The webinar recording can be found here: https://youtu.be/UoX6aoC4fhQ
The multilevel CSA monitoring set of standard core uptake and outcome indicators + expanded indicators linked to a rapid and reliable ICT based data collection instrument to systematically
assess and monitor:
- CSA Adoption/ Access to CIS
- CSA effects on food security and livelihoods household level)
- CSA effects on farm performance
Presented by Harsh Rajpal, Code Partners Pte. Ltd., on 30 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Ciniro Costa Jr., CCAFS, on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Marion de Vries, Wageningen Livestock Research at Wageningen University, on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Issac Emery, Informed Sustainability Consulting, on 29 June 2021 at the second day of the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Hongmin Dong and Sha Wei, Chinese Academy of Agricultural Sciences (CAAS), on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Lini Wollenberg, CCAFS, on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presentation by Han Soethoudt, Jan Broeze, and Heike Axmann of Wageningen University & Resaearch (WUR).
WUR and Olam Rice Nigeria conducted a controlled experiment in Nigeria in which mechanized rice harvesting and threshing were introduced on smallholder farms. The result of the study shows that mechanization considerably reduces losses, has a positive impact on farmers’ income, and the climate.
Learn more: https://www.wur.nl/en/news-wur/show-day/Mechanization-helps-Nigerian-farms-reduce-food-loss-and-increase-income.htm
Presentation on the rapid evidence review findings and key take away messages.
Current evidence for biodiversity and agriculture to achieve and bridging gaps in research and investment to reach multiple global goals.
This presentation was given at an internal workshop in April 2020 and was presented by Le Hoang Anh, Hoang Thi Thien Huong, Le Thi Thanh Huyen, and Nguyen Thi Lien Huong.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
2. weatherData This package helps us to: 1. Get data from weather stations 2. Interpolate weather data for any location
3. Get the packages install.packages("weatherData", repos="http://R-Forge.R-project.org") library(weatherData) install.packages(“cropData", repos="http://R- Forge.R-project.org") library(cropData) OR: http://dl.dropbox.com/u/18619554/cropData_1.0.zip http://dl.dropbox.com/u/18619554/weatherData_1.0.zip
4. Get additional packages install.packages(c(“maps”, “vegan”, “reshape”)) library(maps) library(vegan) library(reshape)
5. Get the script http://dl.dropbox.com/u/18619554/maizeCA.R http://goo.gl/Y6h7m
6. Get the data We will use the Global Summary of Day (GSOD) data of NCDC. ftp://ftp.ncdc.noaa.gov/pub/data/gsod/ Downloading takes a lot of time. However, we can selectively download part of the data, in an automatic way. We will show how to do it with a toy example. Then we will use data from disk to continue.
7. Selecting stations first Select stations within a geographic extent data(stations) locsExtent <-c(0,20,40,60) stationsSelected <- stationsExtent(locsExtent, stations) Show on a map plot(stationsSelected[c("LON","LAT")], pch=3, cex=.5) library(maps) map("world",add=TRUE, interior=F)
8. Download the data Make a working directory first. setwd(“yourFolder”) Now download the files to this working directory. downloadGSOD(2010, 2010, stations = stationsSelected, silent = FALSE, tries = 2, overwrite = FALSE) After a few downloads, kill the process by pressing “Esc”. Inspect what you have in “yourFolder” and delete the downloaded files.
9. Read the data into R Copy the data we have provided you into “yourFolder”. The following lines will make a table and remove missing observations. weather <- makeTableGSOD() weather <- na.omit(weather) fix(weather)
10. Getting some trial data The idea is to link weather data to crop trial data. We get some trial data that was incorporated in the package. trial <- read.csv(system.file("external/trialsCA.csv", package="cropData")) locs <- read.csv(system.file("external/locationsCA.csv", package="cropData"))
11. Make a quick map stationsSelected <- stationsExtent(c(-110,-60,5,25), stations) plot(stationsSelected[c("LON","LAT")], pch=3, cex=.5) points(locs[c("LON","LAT")], pch=15) map("world",add=TRUE, interior=F)
12. Interpolation We have already seen interpolation at work. Now we use interpolation to estimate weather variables for the trial locations. The function interpolateDailyWeather() automatically interpolates the weather surface for each day and extracts the values for each trial location.
13. Interpolate Interpolate weather for the years 2003, 2004 and 2005. ipW2003 <- interpolateDailyWeather( tableGSOD = weatherCA, locations = locs[c("ID", "LON", "LAT", "ALT")], startDate="2003-5-15", endDate="2003-9-25", stations = stationsSelected) Repeat for the other years and then combine: ipW <- rbind(ipW2003,ipW2004,ipW2005)
14. Duration of T > 30 °C = 4.8 h Minimum is assumed to be at sunrise. Maximum is assumed to be 2 h after solar noon. Thermal stress Temperature (°C) Time
15. Derive ecophysiologicalvars ?thermalStressDaily Run the example to see how this works. Then: TEMPSTRESS30 <- thermalStressSeasonal(30, ipW, trial, locs) PREC <- precipitationSeasonal(ipW, trial) RADIATION <- radiationSeasonal(ipW, trial, locs) trial <- cbind(trial, TEMPSTRESS30, PREC, RADIATION)
16. Do RDA on residuals Instead of a normal PCA, we constrain the axes of the PCA with linear combinations of the ecophysiological variables. This type of constrained PCA is called redundancy analysis (RDA)
17. Do ANOVA m <- lm(Yield ~ Variety + Location + Plant.m2, data=tr2005) G + GxE are left over, the rest is filtered out tr2005$Yield <- residuals(m) tr2005 <- tr2005[,c("Variety","Location","Yield")]
20. Putting GxE on map It is possible to use the resulting RDA model to predict for any locations. The steps would be: Interpolate weather variables for new location Derive ecophysiological variables Predict yield value for this new location (not taking into account additive environmental effect)
21. Final remarks Trial data are often noisy – extracting the signal from the data is the objective Many environmental variables are difficult to measure, but can be taken to be “random” in the analysis Many statistical tools exist to link weather data to crop trial data.